特徵縮放 # Simple Linear Regression"""利用簡易線性回歸模型(1個自變數)作薪水預測"""# Importing the libraries"""匯入庫"""importnumpyasnpimportmatplotlib.pyplotaspltimportpandasaspd# Importing the dataset"""匯入數據集"""dataset=pd.read_csv('Salary_Data.csv')X=dataset.iloc[:,:-1].valuesy=dat...
int row, col; extern int get_row(char *filename); extern int get_col(char *filename); extern void get_two_dimension(char *line, double **dataset, char *filename); extern float* evaluate_algorithm(double **dataset, int row, int col, int n_folds); float mean(float* values, int le...
dataset=pd.read_csv(path)#path是数据文件存储路径x=dataset.iloc[:,:1].values y=dataset.iloc[:,1].values#注意上面是小写x,yx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=0)#将训练集打散,这个就不多说,数据预处理里面提到过n_samples=x_train.shape[0]#...
# Importing the librariesimportnumpyasnpimportmatplotlib.pyplotaspltimportpandasaspd# Importing the datasetdataset = pd.read_csv('Salary_Data.csv') X = dataset.iloc[:, :-1].values#除了最后一列的其他列y = dataset.iloc[:,1].values#第二列# Splitting the dataset into the Training set and Tes...
dataset=pd.read_csv('/Users/xiehao/Desktop/100-Days-Of-ML-Code-master/datasets/studentscores.csv')X=dataset.iloc[:,:1].valuesY=dataset.iloc[:,1].values X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=1/4,random_state=0)regressor=LinearRegression()regressor=regressor.fi...
test_set = subset(dataset, split == FALSE) # Feature Scaling # training_set = scale(training_set) # test_set = scale(test_set) # Fitting Simple Linear Regression to the Training set regressor = lm(formula = Salary ~ YearsExperience, ...
SimpleLinearRegression 1 # coding: utf-8 2 3 # In[3]: 4 5 6 import numpy as np 7 import matplotlib.pyplot as plt 8 9 def estimate_coefficients(x, y): 10 # size of the dataset OR number of observations/points 11 n = np.size(x) 12 13 # mean of x and y 14 # Since we ...
A python implementation of linear regression algorithm. (including Maximum Likelihood, Maximum a posterior, Bayesian) - williamd4112/simple-linear-regression
1) Find a small bivariatedataset(preferably from your own discipline) and produce ascatterplot(this is easy using any spreadsheet) 2) Use any statisticstool(a calculator, spreadsheet or statistical package) to calculate the best fitting regression line and test whether the population slope ...
This point is the main difference with simple linear regression. To illustrate how to perform a multiple linear regression in R, we use the same dataset than the one used for simple linear regression (mtcars). Below a short preview: head(dat) ## mpg cyl disp hp drat wt qsec vs am ...